Natural language processing (NLP) is about developing applications and services that are able to understand human languages. In this perfect Natural Language Processing Tutorial, we will use Python NLTK library. Natural language toolkit (NLTK) is the most popular library for natural language processing (NLP) which was written in Python and has a big community behind it. This is the Ultimate guide to learn Natural Language Processing (NLP) basics, such as how to identify and separate words, how to extract topics in a text. You dont need a big and a boring book to start today .
Get Your Copy Now!!Book Objectives
The book objectives include the following:
- To help you appreciate big data as a great source of information and knowledge.
- To help you understand natural language processing.
- To help you know how to use natural language processing to extract knowledge and information from big data.
- To help you learn how to implement natural language processing solutions using NLTK (Natural Language Processing Toolkit) and other libraries in Python.
Who this Book is for?
Do you belong to any of the following categories?
- You are a complete beginner to natural language processing.
- You want to learn Python programming for natural language processing.
- You want to advance your skills in Python for natural language processing.
- Professors, lecturers or tutors who are looking to find better ways to explain Natural Language Processing to their students in the simplest and easiest way.
- Students and academicians, especially those focusing on python programming, Neural Networks, Machine Learning, Deep Learning, and Artificial Intelligence.
If yes, this is the right book for you.
What do you need for this Book?
You only have to have installed Python 3.X on your computer. The author guides you on how to install the rest of the libraries on your computer.
What is inside the book?
- GETTING STARTED WITH NATURAL LANGUAGE PROCESSING
- TEXT WRANGLING AND CLEANSING.
- REPLACING AND CORRECTING WORDS.
- TEXT CLASSIFICATION.
- SENTIMENT ANALYSIS.
- PARSING STRUCTURE IN TEXT.
- SOCIAL MEDIA MINING.
- NLTK FOR SENTIMENT ANALYSIS.
- SCIKIT-LEARN FOR TEXT CLASSIFICATION.
- WORK WITH PDF FILES IN PYTHON.
- WORK WITH TEXT FILES IN PYTHON.
- WORD2VEC ALGORITHM.
- NLP APPLICATIONS
From the back cover.
This comprehensive guide covers both statistical and symbolic approaches to Natural Language Processing. This is a good introduction to all the major topics of computational linguistics, which includes automatic speech recognition and processing, machine translation, information extraction, and statistical methods of linguistic analysis. Indeed, Natural Language Processing is the scientific discipline concerned with making the natural language accessible to machines, and it is a necessary means to facilitate text analytics by establishing structure in unstructured text to enable further analysis. This guide is a fundamental reference for any computational linguist, speech scientist or language data scientist. The explanations and illustrations in this short book are very intuitive and simple. The author helps you understand what natural language processing is. This is basically a theory touching on the fundamentals of natural language processing. The author then explains to you what the NLTK library is and what it does. The rest of the book is about implementing natural language processing tasks using the NLTK library in Python. Samuel Burns uses a combination of theory, Python code examples, and screenshots showing the expected outputs for various program codes.
